Citizens' demand for greater transparency and efficiency in public management reinforces social accountability as a pillar of trust in public institutions and sustainable development. In this context, Supreme Audit Institutions (SAIs) play an essential role in promoting transparency and accountability through their audit activities and their broader social impact.

SAIs' performance is essential in the implementation and evaluation of public policies aimed to achieve national development goals and the Sustainable Development Goals (SDGs), including the fight against climate change. This aligns with Resolution 79/231 of 20241 of the United Nations (UN) General Assembly, which underscores the importance of SAIs in the audit of sustainable development.

In a context of technological transformation and widespread access to information, data has become a strategic source to strengthen transparency and efficiency in public management. SAIs, as guarantors of the responsible use of public funds, must address social challenges and stakeholder’s expectations, adapting to emerging risks and constantly evolving environments. In this scenario, SAIs' capacity to leverage data analysis and evidence-based auditing improves the effectiveness of their audits and enhances their real impact on the quality of life of citizens, helping public management to fulfil its purpose of generating well-being and sustainable development and promoting sustainable progress.

In the context of SAIs as social actors and agents of change, and in an environment of digital transformation, the data-driven approach emerges as a strategic pillar for the transformation of their audit activity. SAIs can enhance their audit methodologies that allow optimising the application of data science disciplines, which allows detecting risks with greater precision, bidirectional interaction with citizens2, evaluating the impact of public policies and strengthening social accountability, and therefore social oversight.

In this way, SAIs contribute with their audit reports to identify opportunities for improving public management

In this context, INTOSAI P-12 (Principle of Value and Benefit of SAIs)3 is of particular relevance, as its Principle 7 establishes SAIs as a credible source of independent and objective knowledge and guidance that supports beneficial changes in the public sector.

The alignment of the recent UN resolution and the principles of INTOSAI P-12 strengthens SAIs' commitment to adopting a data-driven approach, enhancing Performance Audits, and strengthening effective and sustainable social accountability.

This article explores the analytical framework that highlights the significance of the data-driven performance audit, presents case studies of SAIs that have implemented data-driven audit strategies, analyses the opportunities that could strengthen SAIs, and examines how these efforts contribute to sustainable social accountability and, and assesses progress in the contribution to the SDGs.

A data-driven performance audit applies the full audit cycle (planning, execution, reporting and follow-up of recommendations), of techniques and disciplines that facilitate the collection, analysis and generation of data, with precision, based on parameterization and systematization, based on the use of information technologies and the opportunity of the use of cross-cutting strategies in the organization of audit teams.

SAIs can apply a data-driven approach at all stages of the audit cycle, from planning—using risk analysis models and audit portfolios—to execution, where tools such as data analytics, Robotic Process Automation (RPA), and Natural Language Processing (NLP) enhance anomaly detection and improve the tracking of recommendations through indicators, thereby strengthening citizen oversight.

Establishing a 'Data Lab' can maximise the potential of data-driven performance audits. Defined as a cross-cutting data laboratory within the SAI, this unit would be staffed by specialised technicians and equipped with appropriate technologies. Additionally, it could facilitate the execution of coordinated audits with other SAIs or audit institutions.

1. https://www.intosai.org/fileadmin/downloads/documents/open_access/intosai_and_united_nations/79_231 _2024/EN_UN_Resol_79_231.pdf

2. The Social Return of Public Audit. José Antonio Monzó Torrecillas.Silva Solanas Alcaide. Leonardo Visconti Cox (Spanish Court of Audit) - Award for the Best Poster in Communications at the 11th National Congress on Public Sector Auditing in Spain

3. https://www.intosai.org/fileadmin/downloads/documents/open_access/INT_P_11_to_P_99/INTOSAI_P_12/INTOSAI_P_12_en_2019.pdf

Data Labs serve as ideal environments for the development of data science disciplines such as big data, data mining, and ensemble modelling, which enhance the analytical capabilities of SAIs:

• Big data plays a crucial role in the planning phase, enabling the processing and management of vast amounts of structured and unstructured information. This allows SAIs to analyse large datasets efficiently, improving risk assessment and the selection of audit priorities.
• Data mining is especially relevant during the execution phase, as it facilitates the identification of hidden patterns and relationships within extensive data sources. This supports risk detection, anomaly identification, and fraud prevention, enhancing the precision and depth of audits.
• Ensemble modelling is highly valuable during the reporting and follow-up phase, as it combines multiple machine learning models to improve predictive accuracy and reliability. This enhances SAIs' ability to assess the effectiveness of public policies, monitor compliance with recommendations, and anticipate potential risks in future audits.

By integrating these data science disciplines into different stages of the audit cycle, SAIs can strengthen data-driven decision-making, enhance transparency, and improve social accountability.

Figure 1. Data Labs and Enhanced Analytical Capabilities for SAIs

Furthermore, by leveraging these capabilities,Data Labs can generate the following benefits for SAIs:

1. Advancing Data Analytics in Performance Audit.
• Development of risk detection models to identify critical audit areas.
• Implementation of stakeholder identification and classification systems.
• Design of indicators to assess the efficiency and effectiveness of public policies.
• Application of machine learning and data mining techniques to identify patterns in large datasets.
2. Integration of Data from Multiple Sources.
• Cross-referencing public sector databases, and where appropriate, incorporating private sector data.
• Establishment of a centralized data repository accessible to audit teams.
3. Supporting Digital Transformation and Data-driven Culture.
• Providing staff training in data science disciplines for audit teams.
• Creating spaces for experimentation and innovation within SAI.
4. Strengthening Social Transparency and Social Accountability.
• Acilitating the publication of audit results on interactive visualization platforms, enabling citizens to access and utilize public resource management data.
• Encouraging citizen participation through collaborative auditing platforms, where open data serves as a foundation for social oversight
5. Optimizing Audit Processes with Technological Tools
• Dveloping algorithms for the automatic detection of irregularities across all types of audits (operational, financial, and compliance). Advancing the use of artificial intelligence to classify documents from audited entities and streamline the analysis of large datasets.

By integrating data science disciplines through Data Labs into their audit methodologies, SAIs can strengthen social accountability by enhancing transparency, improving risk assessment, and fostering Data-driven decision-making. These advancements enable more accurate and evidence-based audits, empowering citizens with accessible and verifiable information on public resource management. As a result, SAIs reinforce public trust, promote greater civic engagement, and contribute to more responsible and sustainable governance.

Figure 2. Data Labs in Action: Strategic Gains for SAIs

Spanish Court of Audit (SCA) has developed platforms like Local Entities Accountability Platform and Political Parties Accountability Platform which strengthen transparency and facilitate data-driven audits through automation and risk analysis. These platforms are the bases for the development of Smart Auditing Platforms (SAP), as platforms that strengthen the transparency of public management but also enable the identification of risk areas in the public sector, the design of audits based on risk analysis, the execution of audits through RPA and the monitoring of recommendations from audit reports.

In this regard, the SCA is currently working on the development of machine learning techniques based on Artificial Intelligence (AI) tools in the audits of political parties and electoral processes, in order to automate the processing of information contained in standardised documents.

The Federal Court of Accounts (TCU) of Brazil and the National Audit Office (NAO) of the United Kingdom exemplify how the creation of organizational units specialized in data management can significantly enhance the development of audit activity, and therefore of Performance Audits, and thus contribute to social accountability.

TCU's LabContas centralizes government information and uses data analysis techniques to detect irregularities and evaluate the performance of public policies, allowing citizens to access clearer and more verifiable information. In addition, the NAO, through its Analysis Insights Team, uses predictive modelling, statistical analysis and advanced data visualisation to improve public understanding of public management and facilitate more effective social oversight.

The creation of Cross-cutting Data Labs allows SAIs to develop wide-ranging audit projects with a data-driven approach, improving coordination between organizational units with territorial and sectoral criteria. These units reinforce the capacity to conduct Performance Audits with an evaluative approach, particularly in broad public policies such as Education or Health systems, which require a comprehensive and multidisciplinary analysis. They also facilitate inter- institutional collaboration, articulating multiple levels of government, audit entities and key actors, thus guaranteeing more effective oversight aligned with the complexity of these policies,

https://portal.tcu.gov.br/

https://www.nao.org.uk/about-us/teams/analysis-insights-team/

In addition to the development of SPAs and Cross-cutting Data Labs, the opportunity to develop audit frameworks that allow the evaluation of audit areas that require a wide deployment of the data-driven approach stands out.

An example of this need is the work carried out by The Netherlands National Court of Audit (NCA)6, which has developed an audit framework applicable to both government institutions and the private sector, to assess whether algorithms meet established quality criteria and whether the associated risks have been correctly identified and mitigated. This approach shows that, beyond the opportunity to have advanced technological tools and specialized organizational structures, SAIs can design standardized methodologies for the audit of complex areas linked to data science. Integrating these data-driven audit frameworks will enable SAIs to ensure more effective standardized control, risk analysis, and improve social accountability.

Social accountability within the framework of sustainable development, refers as the capacity of present societies to meet the needs of the present without compromising the ability of future generations to meet theirs. SAIs' audit activity should not be limited to assessing regulatory or financial compliance, but should examine whether public resources generate tangible improvements in people's quality of life, reduce inequalities, and promote sustainable and inclusive growth. Through a data-driven approach, Supreme Audit Institutions (SAIs) can provide objective and accessible information on how and to what extent government decisions are contributing to the SDGs, enabling citizens to demand greater accountability and effectiveness in public management.

This citizen expectation reinforces the need for more strategic Performance Audits, based on advanced data analysis, which identify both progress and gaps in the implementation of the SDGs, thereby fostering more transparent and accountable governments.

Within performance auditing, one of the areas where the data-driven approach can generate the greatest impact is in the audit of the measurement of the contribution to the SDGs, due to the large volume of data and key indicators involved. INTOSAI Guide 52027 emphasizes the need to assess not only the financial implementation of public programs, but also their actual impact on sustainable development, which requires the analysis of multiple sources of information. Through advanced big data techniques, data mining, machine learning, and predictive analytics, SAIs can monitor trends, identify gaps, and more accurately assess the degree of achievement of SDGs.

This not only optimizes oversight but also strengthens social accountability, enabling citizens and policymakers to access evidence-based information to demand improvements in public management and the fulfilment of the United Nations 2030 Agenda objectives.

SAIs have a pivotal social role in promoting the efficiency, accountability, effectiveness and transparency of public administration, and thus contribute to the achievement of the SDGs.

A data-driven approach enhances Performance Audits and support their results, and promote social accountability, especially in those that apply the evaluation approach of programs or public policies and/or the measurement of the contribution of the SDGs, due to the vast volume of related data that are involved in the audit cycle of these audits.

By leveraging data as a key driver of this connection, a data-driven performance audit approach reinforces the link between information and social accountability. In this way, SAIs enhance their capacity as transformative agents of social change, contributing to audit reports facilitating the implementation of public policies and measures that improve the quality of life of citizens, and thus driving in the sustainable development of societies, for the benefit of present and future generations.

https://english.rekenkamer.nl/publications/publications/2021/01/26/audit- framework-for-algorithms

https://www.issai.org/wp-content/uploads/2019/08/GUID-5202-Sustainable-Development-The-42Role-of-Supreme-Audit-Institutions.pdf